Autonomous Dual-Arm Manipulation of Familiar Objects

D. Pavlichenko, Diego Rodriguez, Max Schwarz, Christian Lenz, Arul Selvam Periyasamy, Sven Behnke
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引用次数: 12

Abstract

Autonomous dual-arm manipulation is an essential skill to deploy robots in unstructured scenarios. However, this is a challenging undertaking, particularly in terms of perception and planning. Unstructured scenarios are full of objects with different shapes and appearances that have to be grasped in a very specific manner so they can be functionally used. In this paper we present an integrated approach to perform dual-arm pick tasks autonomously. Our method consists of semantic segmentation, object pose estimation, deformable model registration, grasp planning and arm trajectory optimization. The entire pipeline can be executed onboard and is suitable for on-line grasping scenarios. For this, our approach makes use of accumulated knowledge expressed as convolutional neural network models and low-dimensional latent shape spaces. For manipulating objects, we propose a stochastic trajectory optimization that includes a kinematic chain closure constraint. Evaluation in simulation and on the real robot corroborates the feasibility and applicability of the proposed methods on a task of picking up unknown watering cans and drills using both arms.
熟悉物体的自主双臂操纵
自主双臂操作是在非结构化场景中部署机器人的一项基本技能。然而,这是一项具有挑战性的任务,特别是在感知和规划方面。非结构化场景充满了具有不同形状和外观的对象,必须以非常特定的方式掌握这些对象,以便能够有效地使用它们。在本文中,我们提出了一种集成的方法来自主执行双臂拾取任务。该方法包括语义分割、目标姿态估计、可变形模型配准、抓取规划和手臂轨迹优化。整个管道可以在船上执行,适用于在线抓取场景。为此,我们的方法利用了以卷积神经网络模型和低维潜在形状空间表示的积累知识。对于操纵对象,我们提出了一个包含运动链闭合约束的随机轨迹优化。仿真和真实机器人的评估验证了所提方法在用双臂拾取未知水罐和钻头任务中的可行性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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